neural network in a single Tweet

nn.py

from numpy import *
r=random
c=hstack
p=dot
N=r.randn
s=lambda x:1/(1+exp(-x))
a=lambda x:s(x)*(1-s(x))
h,j=X.shape
w=N(5,j+1)
e=N(1,6)
for i in r.randint(0,h,1000*h):o=c((1,X[i]));m=p(w,o);n=c((1,s(m)));d=p(e,n);b=0.02*(Y[i]-s(d));g=b;w+=outer(e[:,1:]*a(d)*a(m),o)*b;e+=n*g*a(d)